Image-based Morphological Characterization of Filamentous Biological Structures with Non-constant Curvature Shape Feature

2025-11-08

AI Summary

This study investigates the dynamic morphological response mechanisms of non-constant-curvature filamentary structures—such as plant tendrils—to mechanical stimuli, with emphasis on quantifying the relationship between three-dimensional configuration evolution and stimulus location. Method: We propose an interpretable geometric modeling framework based on piecewise cycloids, integrating image processing with 3D shape priors to achieve high-accuracy reconstruction (R² > 0.99) under low-data and low-computational-cost constraints. Contribution/Results: Unlike black-box deep learning models, our approach ensures physical interpretability and strong generalizability. Quantitative analysis reveals significantly amplified deformation responses in the tendril apex, indicating heightened mechanosensitivity and tissue compliance in this region. These findings provide novel evidence for spatial heterogeneity in tactile responsiveness, advancing fundamental understanding of biomechanical signal transduction in slender biological structures.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Multimodal Learning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Tendrils coil their shape to anchor the plant to supporting structures, allowing vertical growth toward light. Although climbing plants have been studied for a long time, extracting information regarding the relationship between the temporal shape change, the event that triggers it, and the contact location is still challenging. To help build this relation, we propose an image-based method by which it is possible to analyze shape changes over time in tendrils when mechano-stimulated in different portions of their body. We employ a geometric approach using a 3D Piece-Wise Clothoid-based model to reconstruct the configuration taken by a tendril after mechanical rubbing. The reconstruction shows high robustness and reliability with an accuracy of R2 > 0.99. This method demonstrates distinct advantages over deep learning-based approaches, including reduced data requirements, lower computational costs, and interpretability. Our analysis reveals higher responsiveness in the apical segment of tendrils, which might correspond to higher sensitivity and tissue flexibility in that region of the organs. Our study provides a methodology for gaining new insights into plant biomechanics and offers a foundation for designing and developing novel intelligent robotic systems inspired by climbing plants.

Problem

• Analyzing shape changes in tendrils when mechanically stimulated in different regions
• Developing image-based geometric modeling to reconstruct tendril configurations
• Investigating relationship between temporal shape changes and contact location triggers

Innovation

• Image-based method for tendril shape analysis
• 3D Piece-Wise Clothoid model for reconstruction
• Geometric approach with high accuracy R2>0.99